---
title: NVIDIA
---

The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on
NVIDIA NIM inference microservice. NIM supports models across domains like chat, embedding, and re-ranking models
from the community as well as NVIDIA. These models are optimized by NVIDIA to deliver the best performance on NVIDIA
accelerated infrastructure and deployed as a NIM, an easy-to-use, prebuilt containers that deploy anywhere using a single
command on NVIDIA accelerated infrastructure.

NVIDIA hosted deployments of NIMs are available to test on the [NVIDIA API catalog](https://build.nvidia.com/). After testing,
NIMs can be exported from NVIDIA’s API catalog using the NVIDIA AI Enterprise license and run on-premises or in the cloud,
giving enterprises ownership and full control of their IP and AI application.

NIMs are packaged as container images on a per model basis and are distributed as NGC container images through the NVIDIA NGC Catalog.
At their core, NIMs provide easy, consistent, and familiar APIs for running inference on an AI model.

Below is an example on how to use some common functionality surrounding text-generative and embedding models.

## Installation

```python
pip install -qU langchain-nvidia-ai-endpoints
```

## Setup

**To get started:**

1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models.

2. Click on your model of choice.

3. Under Input select the Python tab, and click `Get API Key`. Then click `Generate Key`.

4. Copy and save the generated key as NVIDIA_API_KEY. From there, you should have access to the endpoints.

```python
import getpass
import os

if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
    nvidia_api_key = getpass.getpass("Enter your NVIDIA API key: ")
    assert nvidia_api_key.startswith("nvapi-"), f"{nvidia_api_key[:5]}... is not a valid key"
    os.environ["NVIDIA_API_KEY"] = nvidia_api_key
```
## Working with NVIDIA API Catalog

```python
from langchain_nvidia_ai_endpoints import ChatNVIDIA

llm = ChatNVIDIA(model="mistralai/mixtral-8x22b-instruct-v0.1")
result = llm.invoke("Write a ballad about LangChain.")
print(result.content)
```

Using the API, you can query live endpoints available on the NVIDIA API Catalog to get quick results from a DGX-hosted cloud compute environment. All models are source-accessible and can be deployed on your own compute cluster using NVIDIA NIM which is part of NVIDIA AI Enterprise, shown in the next section [Working with NVIDIA NIMs](#working-with-nvidia-nims).

## Working with NVIDIA NIMs
When ready to deploy, you can self-host models with NVIDIA NIM—which is included with the NVIDIA AI Enterprise software license—and run them anywhere, giving you ownership of your customizations and full control of your intellectual property (IP) and AI applications.

[Learn more about NIMs](https://developer.nvidia.com/blog/nvidia-nim-offers-optimized-inference-microservices-for-deploying-ai-models-at-scale/)

```python
from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings, NVIDIARerank

# connect to a chat NIM running at localhost:8000, specifying a model
llm = ChatNVIDIA(base_url="http://localhost:8000/v1", model="meta/llama3-8b-instruct")

# connect to an embedding NIM running at localhost:8080
embedder = NVIDIAEmbeddings(base_url="http://localhost:8080/v1")

# connect to a reranking NIM running at localhost:2016
ranker = NVIDIARerank(base_url="http://localhost:2016/v1")
```

## Using NVIDIA AI Foundation Endpoints

A selection of NVIDIA AI Foundation models are supported directly in LangChain with familiar APIs.

The active models which are supported can be found [in API Catalog](https://build.nvidia.com/).

**The following may be useful examples to help you get started:**
- **[`ChatNVIDIA` Model](/oss/integrations/chat/nvidia_ai_endpoints).**
- **[`NVIDIAEmbeddings` Model for RAG Workflows](/oss/integrations/text_embedding/nvidia_ai_endpoints).**
